Biology subjects

Orphan, V.

Publications and source records attributed to Orphan, V..

4 recordsLinked to original sources

bioRxiv2021

Bacterial growth in multicellular aggregates leads to the emergence of complex lifecycles

In response to environmental stresses such as starvation, many bacteria facultatively aggregate into multicellular structures that can attain new metabolic functions and behaviors. Despite the ubiquity and relevance of this form of collective behavior, we lack an understanding of how the spatiotemporal dynamics of aggregate development emerge from cellular physiology. Here, we show that the coupling between growth and spatial gradient formation leads to the emergence of a complex lifecycle, akin to those known for multicellular bacteria. Under otherwise carbon-limited growth conditions, the marine bacterium Vibrio splendidus 12B01 forms multicellular groups to collectively harvest carbon from the brown-algal polysaccharide alginate. This is achieved during growth on dissolved alginate polymer through formation of spherical, clonal clusters of cells that grow up to 40 {micro}m in radius. Clusters develop striking spatial patterning as they grow due to phenotypic differentiation of sub-populations into a shell of static cells surrounding a motile core. Combining in situ measurements of cell physiology with transcriptomics, we show that shell cells express adhesive type IV pili, while motile core cells express carbon storage granules. The emergence of shell and core phenotypes is cued by opposing gradients of carbon and nitrogen that form within cell clusters due to local metabolic activity. Eventually, the shell ruptures, releasing the carbon-storing core, and we show that carbon-storing cells more readily propagate on alginate than non-carbon storing cells. We propose that phenotypic differentiation promotes the resilience of 12B01 groups by enabling clonal groups to grow larger and propagate more effectively. Phenotypic differentiation may be a widespread, but overlooked, strategy among bacteria to enhance resilience in the context of resource limitation.

microbiology↗

bioRxiv2020

Lateral gene transfer drives metabolic flexibility in the anaerobic methane oxidising archaeal family Methanoperedenaceae

Anaerobic oxidation of methane (AOM) is an important biological process responsible for controlling the flux of methane into the atmosphere. Members of the archaeal family Methanoperedenaceae (formerly ANME-2d) have been demonstrated to couple AOM to the reduction of nitrate, iron, and manganese. Here, comparative genomic analysis of 16 Methanoperedenaceace metagenome-assembled genomes (MAGs), recovered from diverse environments, revealed novel respiratory strategies acquired through lateral gene transfer (LGT) events from diverse archaea and bacteria. Comprehensive phylogenetic analyses suggests that LGT has allowed members of the Methanoperedenaceae to acquire genes for the oxidation of hydrogen and formate, and the reduction of arsenate, selenate and elemental sulfur. Numerous membrane-bound multi-heme c type cytochrome complexes also appear to have been laterally acquired, which may be involved in the direct transfer of electrons to metal oxides, humics and syntrophic partners. ImportanceAOM by microorganisms limits the atmospheric release of the potent greenhouse gas methane and has consequent importance to the global carbon cycle and climate change modelling. While the oxidation of methane coupled to sulphate by consortia of anaerobic methanotrophic (ANME) archaea and bacteria is well documented, several other potential electron acceptors have also been reported to support AOM. In this study we identify a number of novel respiratory strategies that appear to have been laterally acquired by members of the Methanoperedenaceae as they are absent in related archaea and other ANME lineages. Expanding the known metabolic potential for members of the Methanoperedenaceae provides important insight into their ecology and suggests their role in linking methane oxidation to several global biogeochemical cycles.

microbiology↗

bioRxiv2019

FIND: Identifying Functionally and Structurally Important Features in Protein Sequences with Deep Neural Networks

The ability to correctly predict the functional role of proteins from their amino acid sequences would significantly advance biological studies at the molecular level by improving our ability to understand the biochemical capability of biological organisms from their genomic sequence. Existing methods that are geared towards protein function prediction or annotation mostly use alignment-based approaches and probabilistic models such as Hidden-Markov Models. In this work we introduce a deep learning architecture (Function Identification with Neural Descriptions or FIND) which performs protein annotation from primary sequence. The accuracy of our methods matches state of the art techniques, such as protein classifiers based on Hidden Markov Models. Further, our approach allows for model introspection via a neural attention mechanism, which weights parts of the amino acid sequence proportionally to their relevance for functional assignment. In this way, the attention weights automatically uncover structurally and functionally relevant features of the classified protein and find novel functional motifs in previously uncharacterized proteins. While this model is applicable to any database of proteins, we chose to apply this model to superfamilies of homologous proteins, with the aim of extracting features inherent to divergent protein families within a larger superfamily. This provided insight into the functional diversification of an enzyme superfamily and its adaptation to different physiological contexts. We tested our approach on three families (nitrogenases, cytochrome bd-type oxygen reductases and heme-copper oxygen reductases) and present a detailed analysis of the sequence characteristics identified in previously characterized proteins in the heme-copper oxygen reductase (HCO) superfamily. These are correlated with their catalytic relevance and evolutionary history. FIND was then applied to discover features in previously uncharacterized members of the HCO superfamily, providing insight into their unique sequence features. This modeling approach demonstrates the power of neural networks to recognize patterns in large datasets and can be utilized to discover biochemically and structurally important features in proteins from their amino acid sequences.\n\nAuthor summary

bioinformatics↗

bioRxiv2018

Divergent methyl-coenzyme M reductase genes in a deep-subseafloor Archaeoglobi

The methyl-coenzyme M reductase (MCR) complex is a key enzyme in archaeal methane generation and has recently been proposed to also be involved in the oxidation of short-chain hydrocarbons including methane, butane and potentially propane. The number of archaeal clades encoding the MCR complex continues to grow, suggesting that this complex was inherited from an ancient ancestor, or has undergone extensive horizontal gene transfer. Expanding the representation of MCR-encoding lineages through metagenomic approaches will help resolve the evolutionary history of this complex. Here, a near-complete Archaeoglobi metagenome-assembled genome (MAG; rG16) was recovered from the deep subseafloor along the Juan de Fuca Ridge flank that encodes two divergent McrABG operons similar to those found in Candidatus Bathyarchaeota and Candidatus Syntrophoarchaeum MAGs. rG16 is basal to members of the class Archaeoglobi, and encodes the genes for {beta}-oxidation, potentially allowing an alkanotrophic metabolism similar to that proposed for Ca. Syntrophoarchaeum. rG16 also encodes a respiratory electron transport chain that can potentially utilize nitrate, iron, and sulfur compounds as electron acceptors. As the first Archaeoglobi with the MCR complex, rG16 extends our understanding of the evolution and distribution of novel MCR encoding lineages among the Archaea.

microbiology↗